Philippa Kaur

To the Editor — A wildlife consumption ban, which China enacted in February as a response to the COVID-19 pandemic, has been welcomed by most conservationists as a step towards avoiding a future outbreak of zoonotic diseases1. There are dissenting voices against this ban, arguing that wildlife generates multiple benefits for people who co-exist with wild species2. While both schools of thought have their own valid arguments, neither has yet to actively lobby for the free, prior and informed consent or consultation of the people who will be directly affected by conservation decisions related to COVID-19.

Throughout the years, indigenous peoples and local communities (IPLCs) have been seen as either culprits of biodiversity decline or as ‘unseen sentinels’ effectively managing and monitoring their territories, which are often highly biodiverse3. This polarized view of IPLCs signals a prevailing lack of understanding of their way of life, where most of their dependence on nature is on a subsistence level. Wildlife consumption is often an essential part of their diets. A blanket ban on wildlife consumption may, therefore, exacerbate food insecurity in these communities. In other cases, IPLC wildlife consumption is more than just for subsistence. It may also have cultural roots and should be respected in that regard. Calling for education campaigns to ‘discredit engrained cultural beliefs’ that lead to wildlife consumption ignores the dynamics of cultural development and would most likely fail to conserve wildlife or fail to prevent another zoonotic disease outbreak4. What is needed is to craft bottom-up solutions together with the IPLCs directly depending on wildlife and to learn from their nuanced understanding of nature.

Through creating opportunities and spaces for dialogue, governments and institutions can involve IPLCs in setting guidelines for wildlife consumption. They can adopt the dialogue approach employed by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), where IPLCs engage in knowledge exchange with technical experts and government representatives5. The dialogue, through parallel contributions of indigenous, local, scientific and practical knowledge, can enhance the understanding of wildlife consumption6. Governments and institutions can tap into the network of non-governmental organizations (NGOs) that closely collaborate with IPLCs and have them facilitate these dialogues. They need to listen carefully to IPLCs, learn from their customary protocols on wildlife use and consumption, and draft laws that could potentially prevent another zoonotic disease outbreak without jeopardizing the livelihoods and well-being of IPLCs. Likewise, IPLCs and civil society can continue to build on processes of self-strengthening and assert themselves in spaces where they can proactively engage in efforts to raise awareness and understanding of traditional wildlife consumption practices. These multiple stakeholders must work together to co-craft potential solutions to this global yet also very local concern of wildlife consumption and its connection to zoonotic diseases.

References

1.
Butler, R. A. Conservationists welcome China’s wildlife trade ban. Mongabay (26 January 2020); https://go.nature.com/3pohgzZ

2.
Roe, D. Despite COVID-19, using wild species may still be the best way to save them. International Institute for Environment and Development (1 April 2020); https://go.nature.com/3pjRNYt

3.
Sheil, D., Boissière, M. & Beaudoin, G. Ecol. Soc. 20, 39 (2015).
Article  Google Scholar 

4.
Ribeiro, J., Bingre, P., Strubbe, D. & Reino, L. Nature 578, 217 (2020).
CAS  Article  Google Scholar 

5.
Hill, R. et al. Curr. Opin. Environ. Sustain. 43, 8–20 (2020).
Article  Google Scholar 

6.
Tengö, M., Brondizio, E. S., Elmqvist, T., Malmer, P. & Spierenburg, M. AMBIO 43, 579–591 (2014).
Article  Google Scholar 

Download references

Author information

Affiliations

Center for Development Research (ZEF) Bonn, University of Bonn, Bonn, Germany
Denise Margaret S. Matias

Institute for Social-Ecological Research (ISOE), Frankfurt am Main, Germany
Denise Margaret S. Matias

Non-Timber Forest Products Exchange Programme (NTFP-EP) Asia, Quezon City, Philippines
Eufemia Felisa Pinto & Diana San Jose

Non-Timber Forest Products Exchange Programme (NTFP-EP) India, c/o Keystone Foundation, Kotagiri, India
Madhu Ramnath

Authors
Denise Margaret S. Matias

Eufemia Felisa Pinto

Madhu Ramnath

Diana San Jose

Contributions
D.M.S.M. conceptualized and drafted the Correspondence. E.F.P. and D.S.J. provided input. M.R. reviewed the Correspondence.
Corresponding author
Correspondence to Denise Margaret S. Matias.

Ethics declarations

Competing interests
The authors declare no competing interests.

Rights and permissions

About this article

Cite this article
Matias, D.M.S., Pinto, E.F., Ramnath, M. et al. Local communities and wildlife consumption bans. Nat Sustain (2020). https://doi.org/10.1038/s41893-020-00662-7
Download citation More

1.
Connell, J. H. & Slatyer, R. O. Mechanisms of succession in natural communities and their role in community stability and organization. Am. Nat. 111, 1119–1144 (1977).
Article  Google Scholar 
2.
Sandin, S. A. & Sala, E. Using successional theory to measure marine ecosystem health. Evol. Ecol. 26, 435–448 (2012).
Article  Google Scholar 

3.
Buma, B., Bisbing, S., Krapek, J. & Wright, G. A foundation of ecology rediscovered: 100 years of succession on the William S. Cooper plots in Glacier Bay, Alaska. Ecology 98, 1513–1523 (2017).
PubMed  Article  PubMed Central  Google Scholar 

4.
Odum, E. P. The strategy of ecosystem development. Science 164, 262–270 (1969).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

5.
Copper, P. Ecological succession in Phanerozoic reef ecosystems: Is it real?. Palaios 3, 136–151 (1988).
ADS  Article  Google Scholar 

6.
Vercelloni, J., Kayal, M., Chancerelle, Y. & Planes, S. Exposure, vulnerability, and resiliency of French Polynesian coral reefs to environmental disturbances. Sci. Rep. 9, 1027 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

7.
Connell, J. H. Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310 (1978).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Grigg, R. Community structure, succession and development of coral reefs in Hawaii. Mar. Ecol. Prog. Ser. 11, 1–14 (1983).
ADS  Article  Google Scholar 

9.
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).
ADS  CAS  Article  Google Scholar 

10.
Knowlton, N. Multiple, “stable” states and the conservation of marine ecosystems. Prog. Oceanogr. 60, 387–396 (2004).
ADS  Article  Google Scholar 

11.
Norström, A. V., Nyström, M., Lokrantz, J. & Folke, C. Alternative states on coral reefs: beyond coral-macroalgal phase shifts. Mar. Ecol. Prog. Ser. 376, 295–306 (2009).
ADS  Article  Google Scholar 

12.
van de Leemput, I. A., Hughes, T. P., van Nes, E. H. & Scheffer, M. Multiple feedbacks and the prevalence of alternate stable states on coral reefs. Coral Reefs 35, 857–865 (2016).
ADS  Article  Google Scholar 

13.
Kitayama, K., Mueller-Dombois, D. & Vitousek, P. M. Primary succession of Hawaiian montane rain forest on a chronosequence of eight lava flows. J. Veg. Sci. 6, 211–222 (1995).
Article  Google Scholar 

14.
Walker, L. R. & del Moral, R. Primary Succession and Ecosystem Rehabilitation. (Cambridge University Press, Cambridge, 2003). https://doi.org/10.1017/CBO9780511615078.

15.
Prach, K. & Walker, L. R. Four opportunities for studies of ecological succession. Trends Ecol. Evol. 26, 119–123 (2011).
PubMed  Article  Google Scholar 

16.
Turner, M. G., Whitby, T. G., Tinker, D. B. & Romme, W. H. Twenty-four years after the Yellowstone Fires: Are postfire lodgepole pine stands converging in structure and function?. Ecology 97, 1260–1273 (2016).
PubMed  Article  Google Scholar 

17.
Rossi, S., Bramanti, L., Gori, A. & Orejas, C. Animal forests of the world: An overview. In Marine Animal Forests (eds. Rossi, S., Bramanti, L., Gori, A. & Orejas, C.) 1–28 (Springer, New York, 2017).

18.
Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).
Article  Google Scholar 

19.
Wilkinson, C. Status of Coral Reefs of the World: 2008. (Global Coral Reef Monitoring Network and Reef and Rainforest Research Center, 2008).

20.
Kittinger, J. N., Finkbeiner, E. M., Glazier, E. W. & Crowder, L. B. Human dimensions of coral reef social-ecological systems. Ecol. Soc. 17, 17 (2012).
Article  Google Scholar 

21.
Bellwood, D. R., Hughes, T. P., Folke, C. & Nyström, M. Confronting the coral reef crisis. Nature 429, 827–833 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

22.
Edmunds, P. J. et al. Persistence and change in community composition of reef corals through present, past, and future climates. PLoS ONE 9, e107525 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

24.
Bruno, J. F. & Selig, E. R. Regional decline of coral cover in the Indo-Pacific: Timing, extent, and subregional comparisons. PLoS ONE 2, e711 (2007).
ADS  PubMed  PubMed Central  Article  Google Scholar 

25.
Adjeroud, M. et al. Recovery of coral assemblages despite acute and recurrent disturbances on a South Central Pacific reef. Sci. Rep. 8, 9680 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Dudgeon, S. R., Aronson, R. B., Bruno, J. F. & Precht, W. F. Phase shifts and stable states on coral reefs. Mar. Ecol. Prog. Ser. 413, 201–216 (2010).
ADS  Article  Google Scholar 

27.
Coles, S. L. & Brown, E. K. Twenty-five years of change in coral coverage on a hurricane impacted reef in Hawai’i: The importance of recruitment. Coral Reefs 26, 705–717 (2007).
ADS  Article  Google Scholar 

28.
Jouffray, J.-B. et al. Identifying multiple coral reef regimes and their drivers across the Hawaiian archipelago. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130268 (2015).
Article  Google Scholar 

29.
Doropoulos, C., Roff, G., Visser, M. S. & Mumby, P. J. Sensitivity of coral recruitment to subtle shifts in early community succession. Ecology 98, 304–314 (2016).
Article  Google Scholar 

30.
Hixon, M. A. & Brostoff, W. N. Succession and herbivory: effects of differential fish grazing on Hawaiian coral-reef algae. Ecol. Monogr. 66, 67–90 (1996).
Article  Google Scholar 

31.
Burkepile, D. E. & Hay, M. E. Impact of herbivore identity on algal succession and coral growth on a Caribbean reef. PLoS ONE 5, e8963 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Humphries, A. T., McClanahan, T. R. & McQuaid, C. D. Differential impacts of coral reef herbivores on algal succession in Kenya. Mar. Ecol. Prog. Ser. 504, 119–132 (2014).
ADS  Article  Google Scholar 

33.
Grigg, R. & Maragos, J. Recolonization of hermatypic corals on submerged lava flows in Hawaii. Ecology 55, 387–395 (1974).
Article  Google Scholar 

34.
Tomascik, T., van Woesik, R. & Mah, A. J. Rapid coral colonization of a recent lava flow following a volcanic eruption, Banda Islands, Indonesia. Coral Reefs 15, 169–175 (1996).
ADS  Article  Google Scholar 

35.
Smallhorn-West, P. F. et al. Coral reef annihilation, persistence and recovery at Earth’s youngest volcanic island. Coral Reefs 39, 529–536 (2020).
Article  Google Scholar 

36.
Edmunds, P. J., Nelson, H. R. & Bramanti, L. Density-dependence mediates coral assemblage structure. Ecology 99, 2605–2613 (2018).
PubMed  Article  PubMed Central  Google Scholar 

37.
Pearson, R. G. Recovery and recolonization of coral reefs. Mar. Ecol. Prog. Ser. 4, 105–122 (1981).
ADS  Article  Google Scholar 

38.
Kayal, M. et al. Predicting coral community recovery using multi-species population dynamics models. Ecol. Lett. 21, 1790–1799 (2018).
PubMed  Article  Google Scholar 

39.
van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nat. Clim. Change. 3, 508–511 (2013).
ADS  Article  CAS  Google Scholar 

40.
Yates, K. K. et al. Diverse coral communities in mangrove habitats suggest a novel refuge from climate change. Biogeosciences 11, 4321–4337 (2014).
ADS  Article  Google Scholar 

41.
Cacciapaglia, C. & van Woesik, R. Reef-coral refugia in a rapidly changing ocean. Glob. Change. Biol. 21, 2272–2282 (2015).
ADS  Article  Google Scholar 

42.
Kavousi, J. & Keppel, G. Clarifying the concept of climate change refugia for coral reefs. ICES J. Mar. Sci. 75, 43–49 (2018).
Article  Google Scholar 

43.
Tanguy, J. C., Bachèlery, P. & LeGoff, M. Archeomagnetism of Piton de la Fournaise: Bearing on volcanic activity at La Réunion Island and geomagnetic secular variation in Southern Indian Ocean. Earth Planet. Sci. Lett. 303, 361–368 (2011).
ADS  CAS  Article  Google Scholar 

44.
Michon, L. & Saint-Ange, F. Morphology of Piton de la Fournaise basaltic shield volcano (La Réunion Island): Characterization and implication in the volcano evolution. J. Geophys. Res. Solid Earth 113, B03203 (2008).
ADS  Article  Google Scholar 

45.
Schleyer, M. H., Benayahu, Y., Parker-Nance, S., van Soest, R. W. M. & Quod, J. P. Benthos on submerged lava beds of varying age off the coast of Reunion, western Indian Ocean: sponges, octocorals and ascidians. Biodiversity 17, 93–100 (2016).
Article  Google Scholar 

46.
Zubia, M. et al. Diversity and assemblage structure of tropical marine flora on lava flows of different ages. Aquat. Bot. 144, 20–30 (2018).
Article  Google Scholar 

47.
Pinault, M. et al. Marine fish communities in shallow volcanic habitats. J. Fish Biol. 82, 1821–1847 (2013).
CAS  PubMed  Article  Google Scholar 

48.
Pinault, M. et al. Fish community structure in relation to environmental variation in coastal volcanic habitats. J. Exp. Mar. Biol. Ecol. 460, 62–71 (2014).
Article  Google Scholar 

49.
Bollard, S. et al. Biodiversity of echinoderms on underwater lava flows with different ages, from the Piton de la Fournaise (Reunion Island, Indian Ocean). Cah. Biol. Mar. 54, 491–497 (2013).
Google Scholar 

50.
Camoin, G. F. et al. Holocene sea level changes and reef development in the southwestern Indian Ocean. Coral Reefs 16, 247–259 (1997).
Article  Google Scholar 

51.
Quod, J.-P. & Bigot, L. Coral Bleaching in the Indian Ocean Islands: Ecological Consequences and Recovery in Madagascar, Comoros, Mayotte and Réunion. Coral Reef Degradation in the Indian Ocean, Status Report. (2000).

52.
Scopélitis, J. et al. Changes of coral communities over 35 years: Integrating in situ and remote-sensing data on Saint-Leu Reef (La Réunion, Indian Ocean). Estuar. Coast. Shelf Sci. 84, 342–352 (2009).
ADS  Article  Google Scholar 

53.
Wickel, J. et al. Coral reef status report for the Western Indian Ocean. Reunion (France). In Coral Reef Status Report for the Western Indian Ocean, Global Coral Reef Monitoring Network (GCRMN)/International Coral Reef Initiative (ICRI) (eds. Obura, D. et al.) 96–108 (2017).

54.
Putts, M. R., Parrish, F. A., Trusdell, F. A. & Kahng, S. E. Structure and development of Hawaiian deep-water coral communities on Mauna Loa lava flows. Mar. Ecol. Prog. Ser. 630, 69–82 (2019).
ADS  Article  Google Scholar 

55.
Loya, Y. Community structure and species diversity of hermatypic corals at Eilat, Red Sea. Mar. Biol. 13, 100–123 (1972).
Article  Google Scholar 

56.
Beenaerts, N. & Vanden Berghe, E. Comparative study of three transect methods to assess coral cover, richness and diversity. West Indian Ocean J. Mar. Sci. 4, 29–37 (2005).
Google Scholar 

57.
Mundy, C. N. An appraisal of methods used in coral recruitment studies. Coral Reefs 19, 124–131 (2000).
Article  Google Scholar 

58.
Adjeroud, M., Penin, L. & Carroll, A. G. Spatio-temporal heterogeneity in coral recruitment around Moorea, French Polynesia: Implications for population maintenance. J. Exp. Mar. Biol. Ecol. 341, 204–218 (2007).
Article  Google Scholar 

59.
Babcock, R. C., Baird, A. H., Piromvaragorn, S., Thomson, D. P. & Willis, B. L. Identification of scleractinian coral recruits from Indo-Pacific reefs. Zool. Stud. 42, 211–226 (2003).
Google Scholar 

60.
Lê, S., Josse, J. & Husson, F. FactoMineR: An R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).
Article  Google Scholar 

61.
Legendre, P. & Legendre, L. Numerical Ecology. 2nd English Ed. (Elsevier Science BV, Amsterdam, 1998).

62.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2018).

63.
Huston, M. A. Patterns of species diversity on coral reefs. Annu. Rev. Ecol. Syst. 16, 149–177 (1985).
Article  Google Scholar 

64.
Connell, J. H., Hughes, T. P. & Wallace, C. C. A 30-year study of coral abundance, recruitment, and disturbance at several scales in space and time. Ecol. Monogr. 67, 461–488 (1997).
Article  Google Scholar 

65.
Karlson, R. H. & Cornell, H. V. Species richness of coral assemblages: Detecting regional influences at local spatial scales. Ecology 83, 452–463 (2002).
Article  Google Scholar 

66.
Adjeroud, M., Poisson, E., Peignon, C., Penin, L. & Kayal, M. Spatial patterns and short-term changes of coral assemblages along a cross-shelf gradient in the southwestern lagoon of New Caledonia. Diversity 11, 21 (2019).
Article  Google Scholar 

67.
Bouchon, C. Quantitative study of the scleractinian coral communities of a fringing reef at Reunion Island (Indian Ocean). Mar. Ecol. Prog. Ser. 4, 273–288 (1981).
ADS  Article  Google Scholar 

68.
Connell, J. H. Population ecology of reef-building corals. In Biology and Geology of Coral Reefs 205–245 (Elsevier, Amsterdam, 1973). https://doi.org/10.1016/B978-0-12-395526-5.50015-8.

69.
Clark, S. & Edwards, A. J. Use of artificial reef structures to rehabilitate reef flats degraded by coral mining in the Maldives. Bull. Mar. Sci. 55, 724–744 (1994).
Google Scholar 

70.
Penin, L. et al. Early post-settlement mortality and the structure of coral assemblages. Mar. Ecol. Prog. Ser. 408, 55–64 (2010).
ADS  Article  Google Scholar 

71.
Darling, E. S., Alvarez-Filip, L., Oliver, T. A., McClanahan, T. R. & Côté, I. M. Evaluating life-history strategies of reef corals from species traits. Ecol. Lett. 15, 1378–1386 (2012).
PubMed  Article  PubMed Central  Google Scholar 

72.
Kayal, M., Vercelloni, J., Wand, M. P. & Adjeroud, M. Searching for the best bet in life-strategy: A quantitative approach to individual performance and population dynamics in reef-building corals. Ecol. Complex. 23, 73–84 (2015).
Article  Google Scholar 

73.
Jouval, F., Latreille, A. C., Bureau, S., Adjeroud, M. & Penin, L. Multiscale variability in coral recruitment in the Mascarene Islands: From centimetric to geographical scale. PLoS ONE 14, e0214163 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Sutherland, J. P. & Karlson, R. H. Development and stability of the fouling community at Beaufort, North Carolina. Ecol. Monogr. 47, 452–446 (1977).
Article  Google Scholar 

75.
Karlson, R. H. & Hurd, L. E. Disturbance, coral reef communities, and changing ecological paradigms. Coral Reefs 12, 117–125 (1993).
ADS  Article  Google Scholar 

76.
Bramanti, L. & Edmunds, P. J. Density-associated recruitment mediates coral population dynamics on a coral reef. Coral Reefs 35, 543–553 (2016).
ADS  Article  Google Scholar  More

Site description and soil sampling
The experiment was established as part of the EFForTS project (Ecological and socioeconomic Functions of tropical lowland rainForest Transformation Systems) in the Jambi province, located in Sumatra, Indonesia8.
The experimental sites are located in the state-owned oil palm plantation PTPNVI, which was planted in 2002 (Fig. 1). All planted palms were derived from Tenera seedlings, which are a crossing between Dura and Psifera palms, supplied by Marihat (Medan, Indonesia). Four different locations (referred to as OM1-4) harbor four treatments, which were established in November 2016. In each of these 16 plots (50 × 50 m), five subplots were randomly established, resulting in 80 samples total.
Fertilizer treatment was conducted in two intensities: for one application the conventional treatment usually used in the entire plantation with 130 kg nitrogen, 25 kg phosphorus and 110 kg potassium ha−1 and reduced fertilization with 68 kg nitrogen, 8.5 kg phosphorous and 93.5 kg potassium ha−1. Additionally, liming was conducted in all plots with equal amounts (213 kg dolomite and 71 kg micromag (micronutrients) ha−1). Fertilizer application and liming was done twice per year. The herbicide treatment used 375 cm3 glyphosate ha−1 sprayed within the palm circle four times per year and 375 cm3 glyphosate ha−1 in inter-rows applied twice per year15. The last application before sampling was done in April 2017. Mechanical weeding was done by cutting vegetation four times per year within the palm circle and two times per year in interrows with a brush cutter. The combination of these applications resulted in four different treatments: conventional fertilization with herbicide spraying (ch), conventional fertilization with mechanical weeding (cw), reduced fertilization with herbicide spraying (rh) and reduced fertilization with mechanical weeding (rw) (Table 1).
Topsoil was sampled in May 2017 with a soil corer from the upper seven centimeters in each subplot with a diameter of five cm. A soil corer was used to take three cores in each subplot with a distance of 1 m to each other and at least 1 m distance to trees. The three bulk soil samples per subplot were homogenized and coarse roots and stones were removed. To prevent nucleic acids, especially RNA, from degradation RNAprotect Bacteria Reagent (Qiagen, Hilden, Germany) was applied in a ratio of 1:1. For measurements of soil parameters, we collected an additional sample, which was not supplemented with RNAprotect solution. All samples were transported in cooling boxes and stored at −80 °C until further use.
Nucleic acid extraction
Frozen samples were thawed on ice. RNAprotect was removed from all samples by centrifuging for 20 min at 804.96 g and 4 °C and discarding the resulting supernatant. DNA and RNA were co-extracted from 1 g of soil by using the Qiagen RNeasy PowerSoil Total RNA kit and the RNeasy PowerSoil DNA Elution kit as recommended by the manufacturer (Qiagen), except that RNA was eluted with 50 µl elution buffer instead of 100 µl. DNA contamination was removed from RNA preparations by using the TurboDNAfree kit (Applied Biosystems, Darmstadt, Germany). For this purpose, 0.1 volume DNAse buffer and 1 µl DNAse were added and incubated for 30 min at 37 °C. Subsequently, a second digestion cycle was performed with 0.5 µl DNAse at 37 °C for 15 min. RNA was then purified with the RNeasy MiniElute Cleanup kit (Qiagen). In order to verify complete DNA removal, a control amplification of the 16 S rRNA gene was performed as described below for 16 S rRNA gene amplification. Purified RNA was then reverse-transcribed into cDNA with the Superscript IV reverse transcriptase and a specific primer (5′-CCGTCAATTCMTTTGAGT-′3) as recommended by the manufacturer (Thermo Fisher Scientific, Schwerte, Germany). After cDNA synthesis, we removed residual RNA by adding 1 µl RNase H (New England Biolabs, Frankfurt am Main, Germany) to each reaction and incubation for 20 min at 37 °C. Obtained DNA and cDNA were stored at −20 °C until further use.
16 S rRNA gene amplification and sequencing
For amplification of 16 S rRNA sequences, we used 16 S rRNA gene primers targeting the V3-V4 region (forward primer: S-D-Bact-0341-b-S-17 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-CCTACGGGNGGCWGCAG-3′, reverse primer: S-D-Bact-0785-a-A-21 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-GACTACHVGGGTATCTAATCC-3′) as described by Klindworth22 and Herlemann23 and added adapters for MiSeq sequencing (underlined). PCR reactions were performed in a total volume 50 µl containing 10 µl of 5-fold Phusion GC buffer, 0.2 µl 50 mM MgCl2 solution, 2.5 µl DMSO, 200 µM of each of the four deoxynucleoside triphosphates and 1 U of Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific). We used 20 to 30 ng of DNA and 1 µl cDNA per reaction. The PCR reaction was started by an initial denaturation at 98 °C for 1 min, followed by 25 cycles of denaturation at 98 °C for 45 s, annealing at 60 °C for 45 s and elongation at 72 °C for 30 s. The final elongation was at 72 °C for 5 minutes. Amplicons were then purified by using MagSi-NGS PREP Plus magnetic beads following the procedure recommended by the manufacturer (Steinbrenner Laborsysteme GmbH, Wiesenbach, Germany) with the Janus Automated Workstation from Perkin Elmer (Perkin Elmer, Waltham Massachusetts, USA). Illumina MiSeq sequencing adapters were attached to the purified amplicons with the Nextera XT Index kit (Illumina, San Diego, USA). The Index PCR was done by using 5 µl of template PCR product, 2.5 µl of each index primer, 12.5 µl of 2x KAPA HiFi HotStart ReadyMix and 2.5 µl PCR grade water. Thermal cycling scheme was as follows: 95 °C for 3 min, 8 cycles of 30 s at 95 °C, 30 s at 55 °C and 30 s at 72 °C and a final extension at 72 °C for 5 min. The indexed products were purified as described before. Products were quantified by using the Quant-iT dsDNA HS assay kit and a Qubit fluorometer following the instructions of the manufacturer (Invitrogen GmbH, Karlsruhe, Germany). Purified amplicons were sequenced by the Göttingen Genomics Laboratory with a MiSeq instrument with a read length of 2 × 300 bp using dual indexing and reagent kit v3 (600 cycles) as recommended by the manufacturer (Illumina).
Sequence processing
We obtained 6,817,019 amplicon sequences with 5,183,993 remaining sequences after quality-filtering from DNA samples. At RNA level 6,412,838 raw sequences with 3,601,637 remaining sequences after quality-filtering were obtained24.
Obtained paired-end sequences were first quality-filtered with fastp version 0.2025 using a minimum phred score of 20, a minimum length of 50 bases, the default sliding window size (–cut_window_size = 4), read correction by overlap (option “correction”), adapter removal of the sequencing primers (option “adapter_fasta”), and the provided index sequences of Illumina. Quality-filtered paired-end reads were merged with PEAR version 0.9.11 and default settings26. Primer sequences were clipped with cutadapt version 2.5 and default settings27. All further steps, except mapping of sequences to ASVs (Amplicon Sequence Variant) were performed with functions implemented in vsearch version 2.1.4.128. Sequences were filtered by size with “sortbylength” with a set minimum length of 300 bp. Dereplication of identical sequences was done by “derep_fulllength”. Denoising and removal of low abundant sequences with less than eight replicates were done with the vsearch UNOISE3 module “cluster_unoise”. Chimeric sequences were removed by employing the UCHIME module of vsearch. This included a de novo chimera removal (“uchime3_denovo”) and a reference-based chimera removal (“uchime_ref”) against the SILVA SSU 138 NR database29. Sequences were mapped to ASVs by vsearch (“usearch_global”) with a set sequence identity threshold of 0.97. Taxonomy assignments were performed with BLASTN30 (version 2.9.0) against the SILVA SSU 138 NR database29 with an minimum identity threshold of 90%31. In addition to the taxonomy identity, we added the taxonomy id of the database, length of fragment, query percentage identity, query coverage and e-value in the taxonomy string of the table. We used identity (pident) and query coverage (qcovs) per ASV of the blast output to exclude uncertain blast hits. As recommended by the SILVA ribosomal RNA database project32, we removed the taxonomic assignment for blast hits if dividing the sum of percent identity and percent query coverage by 2 resulted in ≤93%. In total, 31,987 ASVs were used for downstream analysis.
Bacterial community analysis
The bacterial community composition was further analysed in R33 (version 3.6.1) and RStudio34 (version 1.1.463). ASV counts were normalized by using the Geometric Mean of Pairwise Ratios (GMPR) of the GMPR package version 0.1.335. Community compositions were then analysed by the ampvis2 package version 2.4.11 and “amp_heatmap” at genus level36. The fifteen most abundant genera were displayed as relative abundance and clustered at treatment level. Heat-trees were displayed by the metacoder37 package (version 0.3.2.9001).
For heat-tree calculation all counts were summed at order level and all taxa with a relative abundance of More

1.
Nazni, W. A. et al. Establishment of Wolbachia Strain wAlbB in Malaysian populations of Aedes aegypti for Dengue control. Curr. Biol. 29, 4241–4248 (2019). e4245.
CAS  Article  Google Scholar 
2.
Ryan, P. A. et al. Establishment of wMel Wolbachia in Aedes aegypti mosquitoes and reduction of local dengue transmission in Cairns and surrounding locations in northern Queensland, Australia. Gates Open Res. 3, 1547 (2019).
Article  Google Scholar 

3.
King, J. G., Souto-Maior, C., Sartori, L. M., Maciel-de-Freitas, R. & Gomes, M. G. M. Variation in Wolbachia effects on Aedes mosquitoes as a determinant of invasiveness and vectorial capacity. Nat. Commun. 9, 1483 (2018).
ADS  Article  Google Scholar 

4.
Souto-Maior, C., Sylvestre, G., Braga Stehling Dias, F., Gomes, M. G. M. & Maciel-de-Freitas, R. Model-based inference from multiple dose, time course data reveals Wolbachia effects on infection profiles of type 1 dengue virus in Aedes aegypti. PLoS Negl. Trop. Dis. 12, e0006339 (2018).
Article  Google Scholar 

5.
Ferguson, N. M. et al. Modeling the impact on virus transmission of Wolbachia-mediated blocking of dengue virus infection of Aedes aegypti. Sci. Transl. Med. 7, 279ra237 (2015).
Article  Google Scholar 

6.
Ant, T. H., Herd, C. S., Geoghegan, V., Hoffmann, A. A. & Sinkins, S. P. The Wolbachia strain wAu provides highly efficient virus transmission blocking in Aedes aegypti. PLoS Pathog. 14, e1006815 (2018).
Article  Google Scholar 

7.
Walker, T. et al. The wMel Wolbachia strain blocks dengue and invades caged Aedes aegypti populations. Nature 476, 450–453 (2011).
ADS  CAS  Article  Google Scholar 

8.
Frentiu, F. D. et al. Limited dengue virus replication in field-collected Aedes aegypti mosquitoes infected with Wolbachia. PLoS Negl. Trop. Dis. 8, e2688 (2014).
Article  Google Scholar 

9.
Moreira, L. A. et al. A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya, and Plasmodium. Cell 139, 1268–1278 (2009).
Article  Google Scholar 

10.
Fraser, J. E. et al. Novel Wolbachia-transinfected Aedes aegypti mosquitoes possess diverse fitness and vector competence phenotypes. PLoS Pathog. 13, e1006751 (2017).
Article  Google Scholar 

11.
Joubert, D. A. et al. Establishment of a Wolbachia superinfection in Aedes aegypti Mosquitoes as a potential approach for future resistance management. PLoS Pathog. 12, e1005434 (2016).
Article  Google Scholar 

12.
Bian, G., Xu, Y., Lu, P., Xie, Y. & Xi, Z. The endosymbiotic bacterium Wolbachia induces resistance to dengue virus in Aedes aegypti. PLoS Pathog. 6, e1000833 (2010).
Article  Google Scholar 

13.
Chouin-Carneiro, T. et al. Wolbachia strain wAlbA blocks Zika virus transmission in Aedes aegypti. Med. Vet. Entomol. https://doi.org/10.1111/mve.12384 (2019).
Article  PubMed  PubMed Central  Google Scholar 

14.
Pacidonio, E. C., Caragata, E. P., Alves, D. M., Marques, J. T. & Moreira, L. A. The impact of Wolbachia infection on the rate of vertical transmission of dengue virus in Brazilian Aedes aegypti. Parasit. Vectors 10, 296 (2017).
Article  Google Scholar 

15.
Duong, V. et al. Asymptomatic humans transmit dengue virus to mosquitoes. Proc. Natl Acad. Sci. USA 112, 14688–14693 (2015).
ADS  CAS  Article  Google Scholar  More

1.
Hugelius, G. et al. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11, 6573–6593 (2014).
Google Scholar 
2.
Harden, J. W. et al. Field information links permafrost carbon to physical vulnerabilities of thawing. Geophys. Res. Lett. 39, L15704 (2012).
Google Scholar 

3.
Schädel, C. et al. Circumpolar assessment of permafrost C quality and its vulnerability over time using long-term incubation data. Glob. Change Biol. 20, 641–652 (2014).
Google Scholar 

4.
Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).
Google Scholar 

5.
Koven, C. D. et al. A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback. Philos. Trans. R. Soc. A 373, 20140423 (2015).
Google Scholar 

6.
Nannipieri, P. et al. Microbial diversity and soil functions. Eur. J. Soil Sci. 68, 12–26 (2003).
Google Scholar 

7.
Harding, T., Jungblut, A. D., Lovejoy, C. & Vincent, W. F. Microbes in High Arctic snow and implications for the cold biosphere. Appl. Environ. Microbiol. 77, 3234–3243 (2011).
Google Scholar 

8.
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
Google Scholar 

9.
Bier, R. L. et al. Linking microbial community structure and microbial processes: an empirical and conceptual overview. FEMS Microbiol. Ecol. 91, fiv113 (2015).
Google Scholar 

10.
Nunan, N., Leloup, J., Ruamps, L. S., Pouteau, V. & Chenu, C. Effects of habitat constraints on soil microbial community function. Sci. Rep. 7, 4280 (2017).
Google Scholar 

11.
Graham, E. B. et al. Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes? Front. Microbiol. 7, 214 (2016).
Google Scholar 

12.
Schimel, J. in Arctic and Alpine Biodiversity: Patterns, Causes and Ecosystem Consequences (eds Chapin, F. S. & Körner, C.) 239–254 (Springer, 1995).

13.
Schimel, J. & Schaeffer, S. M. Microbial control over carbon cycling in soil. Front. Microbiol. 3, 348 (2012).
Google Scholar 

14.
Bottos, E. M. et al. Dispersal limitation and thermodynamic constraints govern spatial structure of permafrost microbial communities. FEMS Microbiol. Ecol. 94, fiy110 (2018).
Google Scholar 

15.
Jansson, J. K. & Tas, N. The microbial ecology of permafrost. Nat. Rev. Microbiol. 12, 414–425 (2014).
Google Scholar 

16.
Mackelprang, R. et al. Microbial survival strategies in ancient permafrost: insights from metagenomics. ISME J. 11, 2305–2318 (2017).
Google Scholar 

17.
Philippot, L. et al. Loss in microbial diversity affects nitrogen cycling in soil. ISME J. 7, 1609–1619 (2013).
Google Scholar 

18.
Monteux, S. et al. Long-term in situ permafrost thaw effects on bacterial communities and potential aerobic respiration. ISME J. 12, 2129–2141 (2018).
Google Scholar 

19.
Johnston, E. R. et al. Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths. Proc. Natl Acad. Sci. USA 116, 15096–15105 (2019).
Google Scholar 

20.
Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl Acad. Sci. USA 109, 21390–21395 (2012).
Google Scholar 

21.
Sanders, T., Fiencke, C., Hüpeden, J., Pfeiffer, E. M. & Spieck, E. Cold adapted Nitrosospira sp.: a potential crucial contributor of ammonia oxidation in cryosols of permafrost-affected landscapes in Northeast Siberia. Microorganisms 7, 699 (2019).
Google Scholar 

22.
Hill, K. A. et al. Processing of atmospheric nitrogen by clouds above a forest environment. J. Geophys. Res. Atmos. 112, D11301 (2007).
Google Scholar 

23.
Knoblauch, C., Beer, C., Sosnin, A., Wagner, D. & Pfeiffer, E.-M. Predicting long-term carbon mineralization and trace gas production from thawing permafrost of Northeast Siberia. Glob. Change Biol. 19, 1160–1172 (2013).
Google Scholar 

24.
Wild, B. et al. Plant-derived compounds stimulate the decomposition of organic matter in Arctic permafrost soils. Sci. Rep. 6, 25607 (2016).
Google Scholar 

25.
Strauss, J. et al. Deep Yedoma permafrost: a synthesis of depositional characteristics and carbon vulnerability. Earth-Sci. Rev. 172, 75–86 (2017).
Google Scholar 

26.
Wertz, S. et al. Maintenance of soil functioning following erosion of microbial diversity. Environ. Microbiol. 8, 2162–2169 (2006).
Google Scholar 

27.
Fontaine, S. et al. Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature 450, 277–280 (2007).
Google Scholar 

28.
Rillig, M. C. et al. Interchange of entire communities: microbial community coalescence. Trends Ecol. Evol. 30, 470–476 (2015).
Google Scholar 

29.
Mackelprang, R. et al. Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368–371 (2011).
Google Scholar 

30.
Keuper, F. et al. A frozen feast: thawing permafrost increases plant-available nitrogen in subarctic peatlands. Glob. Change Biol. 18, 1998–2007 (2012).
Google Scholar 

31.
Elberling, B., Christiansen, H. H. & Hansen, B. U. High nitrous oxide production from thawing permafrost. Nat. Geosci. 3, 332–335 (2010).
Google Scholar 

32.
Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature 528, 504–509 (2015).
Google Scholar 

33.
Gittel, A. et al. Distinct microbial communities associated with buried soils in the Siberian tundra. ISME J. 8, 841–853 (2014).
Google Scholar 

34.
Weiss, N. et al. Thermokarst dynamics and soil organic matter characteristics controlling initial carbon release from permafrost soils in the Siberian Yedoma region. Sediment. Geol. 340, 38–48 (2016).
Google Scholar 

35.
Inglese, C. N. et al. Examination of soil microbial communities after permafrost thaw subsequent to an active layer detachment in the High Arctic. Arct. Antarct. Alp. Res. 49, 455–472 (2017).
Google Scholar 

36.
Wild, B. et al. Microbial nitrogen dynamics in organic and mineral soil horizons along a latitudinal transect in Western Siberia. Glob. Biogeochem. Cycles 29, 567–582 (2015).
Google Scholar 

37.
Voigt, C. et al. Increased nitrous oxide emissions from Arctic peatlands after permafrost thaw. Proc. Natl Acad. Sci. USA 114, 6238–6243 (2017).
Google Scholar 

38.
Wrage-Mönnig, N. et al. The role of nitrifier denitrification in the production of nitrous oxide revisited. Soil Biol. Biochem. 123, A3–A16 (2018).
Google Scholar 

39.
Siljanen, H. M. P. et al. Archaeal nitrification is a key driver of high nitrous oxide emissions from Arctic peatlands. Soil Biol. Biochem. 137, 107539 (2019).
Google Scholar 

40.
Voigt, C. et al. Nitrous oxide emissions from permafrost-affected soils. Nat. Rev. Earth Environ. 1, 420–434 (2020).
Google Scholar 

41.
Keuper, F. et al. Experimentally increased nutrient availability at the permafrost thaw front selectively enhances biomass production of deep-rooting subarctic peatland species. Glob. Change Biol. 23, 4257–4266 (2017).
Google Scholar 

42.
Liu, X.-Y. et al. Nitrate is an important nitrogen source for Arctic tundra plants. Proc. Natl Acad. Sci. USA 115, 3398–3403 (2018).
Google Scholar 

43.
Myrstener, M. et al. Persistent nitrogen limitation of stream biofilm communities along climate gradients in the Arctic. Glob. Change Biol. 24, 3680–3691 (2018).
Google Scholar 

44.
Knoblauch, C., Beer, C., Liebner, S., Grigoriev, M. N. & Pfeiffer, E.-M. Methane production as key to the greenhouse gas budget of thawing permafrost. Nat. Clim. Change 8, 309–312 (2018).
Google Scholar 

45.
Holm, S. et al. Methanogenic response to long-term permafrost thaw is determined by paleoenvironment. FEMS Microbiol. Ecol. 96, fiaa021 (2020).
Google Scholar 

46.
Douglas, T. A. et al. Biogeochemical and geocryological characteristics of wedge and thermokarst-cave ice in the CRREL permafrost tunnel, Alaska. Permafr. Periglac. Process. 22, 120–128 (2011).
Google Scholar 

47.
Long, A. & Péwé, T. L. Radiocarbon dating by high-sensitivity liquid scintillation counting of wood from the Fox permafrost tunnel near Fairbanks, Alaska. Permafr. Periglac. Process. 7, 281–285 (1996).
Google Scholar 

48.
Hamilton, T. D., Craig, J. L. & Sellmann, P. V. The Fox permafrost tunnel: a late Quaternary geologic record in central Alaska. GSA Bull. 100, 948–969 (1988).
Google Scholar 

49.
Shur, Y., French, H. M., Bray, M. T. & Anderson, D. A. Syngenetic permafrost growth: cryostratigraphic observations from the CRREL tunnel near Fairbanks, Alaska. Permafr. Periglac. Process. 15, 339–347 (2004).
Google Scholar 

50.
Howard, M. M., Bell, T. H. & Kao-Kniffin, J. Soil microbiome transfer method affects microbiome composition, including dominant microorganisms, in a novel environment. FEMS Microbiol. Lett. 364, fnx092 (2017).
Google Scholar 

51.
Patra, A. K. et al. Effects of grazing on microbial functional groups involved in soil N dynamics. Ecol. Monogr. 75, 65–80 (2005).
Google Scholar 

52.
Fontaine, S. et al. Fungi mediate long term sequestration of carbon and nitrogen in soil through their priming effect. Soil Biol. Biochem. 43, 86–96 (2011).
Google Scholar 

53.
Elberling, B. et al. Long-term CO2 production following permafrost thaw. Nat. Clim. Change 3, 890–894 (2013).
Google Scholar 

54.
Walz, J., Knoblauch, C., Böhme, L. & Pfeiffer, E.-M. Regulation of soil organic matter decomposition in permafrost-affected Siberian tundra soils—impact of oxygen availability, freezing and thawing, temperature, and labile organic matter. Soil Biol. Biochem. 110, 34–43 (2017).
Google Scholar 

55.
Weedon, J. T. et al. Temperature sensitivity of peatland C and N cycling: does substrate supply play a role? Soil Biol. Biochem. 61, 109–120 (2013).
Google Scholar 

56.
Ping, C. L. Soil temperature profiles of two Alaskan soils. Soil Sci. Soc. Am. J. 51, 1010–1018 (1987).
Google Scholar 

57.
D’Amico, S. et al. Psychrophilic microorganisms: challenges for life. EMBO Rep. 7, 385–389 (2006).
Google Scholar 

58.
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).
Google Scholar 

59.
Wu, J., Joergensen, R. G., Pommerening, B., Chaussod, R. & Brookes, P. C. Measurement of soil microbial biomass C by fumigation–extraction—an automated procedure. Soil Biol. Biochem. 22, 1167–1169 (1990).
Google Scholar 

60.
Rotthauwe, J. H., Witzel, K. P. & Liesack, W. The ammonia monooxygenase structural gene amoA as a functional marker: molecular fine-scale analysis of natural ammonia-oxidizing populations. Appl. Environ. Microbiol. 63, 4704–4712 (1997).
Google Scholar 

61.
Tourna, M., Freitag, T. E., Nicol, G. W. & Prosser, J. I. Growth, activity and temperature responses of ammonia-oxidizing archaea and bacteria in soil microcosms. Environ. Microbiol. 10, 1357–1364 (2008).
Google Scholar 

62.
Fowler, S. J., Palomo, A., Dechesne, A., Mines, P. D. & Smets, B. F. Comammox Nitrospira are abundant ammonia oxidizers in diverse groundwater-fed rapid sand filter communities. Environ. Microbiol. 20, 1002–1015 (2018).
Google Scholar 

63.
Pjevac, P. et al. AmoA-targeted polymerase chain reaction primers for the specific detection and quantification of comammox Nitrospira in the environment. Front. Microbiol. 8, 1508 (2017).
Google Scholar 

64.
Muyzer, G., Waal, E. Cde & Uitterlinden, A. G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695–700 (1993).
Google Scholar 

65.
Bartram, A. K., Lynch, M. D. J., Stearns, J. C., Moreno-Hagelsieb, G. & Neufeld, J. D. Generation of multimillion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired-end Illumina reads. Appl. Environ. Microbiol. 77, 3846–3852 (2011).
Google Scholar 

66.
Smith, D. P. & Peay, K. G. Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PLoS ONE 9, e90234 (2014).
Google Scholar 

67.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Google Scholar 

68.
Morgan, M. et al. ShortRead: a Bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25, 2607–2608 (2009).
Google Scholar 

69.
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).
Google Scholar 

70.
Haas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21, 494–504 (2011).
Google Scholar 

71.
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).
Google Scholar 

72.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Google Scholar 

73.
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
Google Scholar 

74.
Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).
Google Scholar 

75.
McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).
Google Scholar 

76.
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
Google Scholar 

77.
Lagkouvardos, I., Fischer, S., Kumar, N. & Clavel, T. Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons. PeerJ 5, e2836 (2017).
Google Scholar 

78.
White, D. C., Davis, W. M., Nickels, J. S., King, J. D. & Bobbie, R. J. Determination of the sedimentary microbial biomass by extractible lipid phosphate. Oecologia 40, 51–62 (1979).
Google Scholar 

79.
Olsson, P. A., Bååth, E., Jakobsen, I. & Söderström, B. The use of phospholipid and neutral lipid fatty acids to estimate biomass of arbuscular mycorrhizal fungi in soil. Mycol. Res. 99, 623–629 (1995).
Google Scholar 

80.
Ruess, L. & Chamberlain, P. M. The fat that matters: soil food web analysis using fatty acids and their carbon stable isotope signature. Soil Biol. Biochem. 42, 1898–1910 (2010).
Google Scholar 

81.
Zelles, L. Fatty acid patterns of phospholipids and lipopolysaccharides in the characterisation of microbial communities in soil: a review. Biol. Fertil. Soils 29, 111–129 (1999).
Google Scholar 

82.
Frostegård, A. & Bååth, E. The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil. Biol. Fertil. Soils 22, 59–65 (1996).
Google Scholar 

83.
Lenth, R. Least-squares means: the R package lsmeans. J. Stat. Softw. 69, 1–33 (2016).
Google Scholar 

84.
Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund—an R package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012).
Google Scholar 

85.
Warton, D. I., Wright, S. T. & Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3, 89–101 (2012).
Google Scholar 

86.
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Google Scholar 

87.
McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 10, e1003531 (2014).
Google Scholar 

88.
Pinto, A. J. et al. Metagenomic evidence for the presence of comammox Nitrospira-like bacteria in a drinking water system. mSphere 1, e00054-15 (2016).
Google Scholar 

89.
Kozlowski, J. A., Kits, K. D. & Stein, L. Y. Comparison of nitrogen oxide metabolism among diverse ammonia-oxidizing bacteria. Front. Microbiol. 7, 1090 (2016).
Google Scholar 

90.
Kits, K. D. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature 549, 269–272 (2017).
Google Scholar 

91.
Kuhn, M. caret: Classification and Regression Training v.6.0-86 (2020); https://CRAN.R-project.org/package=caret

92.
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Google Scholar 

93.
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn (Springer, 2009).

94.
Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. VSURF: an R package for variable selection using random forests. R J. 7, 19–33 (2015).
Google Scholar 

95.
R Core Team. R: a Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019). More

Basic characters of the six chloroplast genomes
The cp genomes of P. cicutarrifolia, P. hubeiensis, P. jiugongshanensis, P. merrilliana and P. ranunculoides (GenBank accessions: MT268974, MT268976, MT937162, MT268977, MT268978) were reported for the first time here, and that of P. filchnerae was downloaded from NCBI (MK88869821).
The sequencing coverage of our five newly assembled cp genomes was from 923 to 6237 (Figure S1). The six cp genomes possessed typical quadripartite structure: IRa, IRb, LSC and SSC (Table 1), and they exhibited the same gene order, no gene rearrangement or inversion occurred (Figure S2). The physical map of the cp genome of P. hubeiensis was shown in Fig. 1. The GC content was ~ 37%. The newly sequenced genomes ranged from 150,187 bp to 151,972 bp, harboring 113 genes: four ribosomal RNA genes, 29 tRNA genes and 80 protein-coding genes, and among them 14 genes was duplicated in IRa and IRb (Table 1). Due to presence of multiple stop codons, the gene infA was pseudogenized in the five newly sequenced species. The open reading frame (ORF) in accD of P. filchnerae (MK888698) was truncated to be only 1305 bp compared with 1455 or 1464 bp ORF of other five species. Lee et al.34 identified five conserved amino acid sequence motifs in accD gene. Conserved amino acid sequence motifs IV and V were absent in accD of P. filchnerae. Therefore, accD was nonfunctional in P. filchnerae.
Table 1 Basic characteristics of cp genomes of the six Primula species (Pc: P. cicutarrifolia; Pf: P. filchnerae; Ph: P. hubeiensis; Pj: P. jiugongshanensis; Pm: P. merrilliana; Pr: P. ranunculoides).
Full size table

Figure 1

Physical map of the P. hubeiensis chloroplast genome.

Full size image

SSRs and repeats
Five categories of SSRs were identified for the six species (Table 2). The least number of SSRs was 41 for P. ranunculoides and the most 59 for P. merrilliana. Three types of SSRs were detected for P. filchnerae, and in the rest species four types could be found. While mono-, di- and tetra-nucleotide repeats existed across all the six species, tri- and penta-inucleotide repeats resided in three and two species respectively. Mono- and dinucleotide repeats accounted for the vast majority of SSRs (65.1% for P. cicutariifolia, 87.5% for P. filchnerae, 69.0% for P. hubeiensis, 62.8% for P. jiugongshanensis, 72.9% for P. merrilliana, 73.2% for P. ranunculoides). Most or all mono- repeats were A/T repeats including 10 to 16 nucleotides. The number of repeat units ranged from five to eight for dinucleotide repeats. The tri- and penta-nucleotide SSRs consisted of four motifs, and tetra-nucleotide SSRs of four to five repeat units.
Table 2 Types and numbers of SSRs in the cp genomes of six Primula species, the numbers in the bracket indicating total number of SSRs (Pc: P. cicutarrifolia; Pf: P. filchnerae; Ph: P. hubeiensis; Pj: P. jiugongshanensis; Pm: P. merrilliana; Pr: P. ranunculoides).
Full size table

Except the largest repeat for each genome (i.e. IRs), a total of 183 repeat pairs (three types: forward (F), reverse (R), and palindromic repeats (P)) were detected in the six genomes (Fig. 2), which ranged from 30 to 137 bp in length. Palindromic repeats were the most common, accounting for 55.2% (101 of 183), followed by forward repeats (44.3%, 81 of 183). No complement repeats were identified in all species and one pair of reverse repeats existed specifically in P. ranunculoides. In the six species, 96.7% (177 of 183 repeat pairs) repeats were 30–59 bp in length, consistent with the length reported in other Primula species20. The longest repeat (137 bp) was found in P. cicutariifolia, and this species contained the most repeats (44 pairs), while P. jiugongshanensis had the least (24 pairs).
Figure 2

Types and numbers of repeat pairs in the cp genomes of six Primula species (Pc: P. cicutarrifolia; Pf: P. filchnerae; Ph: P. hubeiensis; Pj: P. jiugongshanensis; Pm: P. merrilliana; Pr: P. ranunculoides).

Full size image

IR/SC boundary
The IR/SC boundary regions of the six Primula cp genomes were compared, and the IR/SC junction regions showed slight differences in the length of organization genes flanking the junctions or the distance between the junctions and the organization genes (Fig. 3). The genes spanning or flanking the junction of LSC/IRb, IRb/SSC, SSC/IRa and IRa/LSC were rps19/rpl2, ndhF, ycf1, rpl2/trnH, respectively. IR expansion and contraction was observed. P. cicutarrifolia had the smallest size of IR but largest size of both LSC and SSC; though largest size of IR was detected in P. filchnerae, the LSC or SSC was not the smallest in this species. The gene trnH was located in LSC, 0–24 bp away from the IRa/LSC border. The largest extensions of ycf1 into both SSC and IRa occurred in P. filchnerae (4566 bp and 1023 bp, respectively) and ycf1 of P. filchnerae were the longest among the six species. The gene ndhF was utterly situated in SSC and 108 bp distant from the IRb/SSC junction in P. cicutarrifolia; in the rest five species the fragment size of ndhF in SSC was largest in P. hubeiensis (2194 bp). In P. cicutarrifolia, P. jiugongshanensis and P. merrilliana, rps19 and rpl2 were located in the upstream and downstream of the LSC/IRb junction, respectively; rps19 ran across the LSC/IRb junction in P. filchnerae, P. hubeiensis, P. ranunculoides with 161, 62, 56 bp extension in IRb, respectively.
Figure 3

LSC/IR, and SSC/IR border regions of the six Primula cp genomes.

Full size image

Divergent hotspots in the Primula chloroplast genome
As indicated by the value of Pi, the nucleotide variability of the 22 Primula species (Table S1) was evaluated by DnaSP 6.1231 using noncoding sequences (intron and intergenic spacer) or protein coding sequences (CDS) at least 200 bp long. The variation level of DNA polymorphorism was 0.00444–0.11369 for noncoding sequences or 0.00094–0.05036 for CDSs. For the CDSs, the highest Pi value were detected for ycf1 (0.05036), followed by matK (0.04878), rpl22 (0.04364), ndhF (0.03975), rps8 (0.03658), ndhD (0.03455), ccsA (0.03292), rpl33 (0.0303), rps15 (0.03022), and rpoC2 (0.02954). These markers had higher Pi than rbcL (0.02149). Obviously, the gene ycf1 exhibited the greatest diversity and harbored the most abundant variation. The ten most divergent regions among noncoding regons included trnH (GUG)-psbA (0.11369), trnW (CCA)-trnP (UGG) (0.09463), rpl32-trnL (UAG) (0.09337), ndhC-trnV (UAC) (0.09148), ccsA-ndhD (0.08745), ndhG-ndhI (0.08363), trnK (UUU)-rps16 (0.08334), trnM (CAU)-atpE (0.08273), trnS (GGA)-rps4 (0.08028), and trnC (GCA)-petN (0.07971). No intron ranked among the top ten variable noncoding regions.
Phylogenetic analysis
The ML tree of 22 Primula species was constructed with RAxML32 (Fig. 4), based on the whole cp genomes. The six pinnate-leaved Primula species did not form a monophyly, but separated into two distant clades. P. filchnerae grouped with P. sinensis, the other five species clustered together and constituted the clade Sect. Ranunculoides with 100% bootstrap. In the ML tree, Sect. Proliferae exhibited monophyly, while species of Sect. Crystallophlomis separated into different clades.
Figure 4

ML phylogenetic tree of Primula species based on cp genomes. Bootstrap support at nodes are all 100%.

Full size image

The topology of the ML tree based on ycf1 (Figure S3) was consistent with that based on whole cp genomes (Fig. 4), except that the clade formed by P. veris and P. knuthiana were sister to the clade consisting of Sects. Auganthus, Obconicolisteri, Carolinella and Monocarpicae instead of being sister to the clade of Sects. Proliferae, Ranunculoides and Crystallophlomis.
We also constructed both ML and NJ tree of 71 Primula species based on the concatenation of three common barcoding markers (ITS, matK and rbcL). Only the results of NJ analysis (Fig. 5) showed consistency with those of Yan et al.12, Liu et al.35, and ML analysis based on whole cp genomes (Fig. 4). The six pinnate-leaved Primula species were separated into two distantly related groups. The clade consisting of P. filchnerae and P. sinensis (Sect. Auganthus) was sister to the clade formed by Sects. Carolinella, Obconicolisteri, Monocarpicae, Cortusoides, Malvacea, Pycnoloba. The five pinnatisect-leaved species P. cicutarrifolia, P. hubeiensis, P. jiugonshanensis, P. merrilliana and P. ranunculoides (Sect. Ranunculoides) comprised a 100% supported clade, which was sister to the group containing Sects. Crystallophlomis, Petiolares, Proliferae, Amethystina. Sect. Carolinella and Sect. Crystallophlomis, and Sect. Malvacea were polyphyletic.
Figure 5

NJ bootstrap consensus tree of Primula based on concatenation of ITS, matK and rbcL.

Full size image More